NDLI: Stochastic collision detection between deformable models using particle swarm optimization algorithm

Content Provider	IEEE Xplore Digital Library
Author	Wang Tianzhu Li Wenhui Wang Yi Ge Zihou Han Dongfeng
Copyright Year	2006
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun (Wang Tianzhu; Li Wenhui; Wang Yi; Ge Zihou; Han Dongfeng)
Abstract	We present an efficient algorithm for detecting collisions and self-collisions between highly deformable mass models, which is a combination of newly developed stochastic method and particle swarm optimization (PSO) algorithm. In stochastic collision detection, user can balance performance and detection quality by sampling primitive pairs within the models. To accelerate detecting process in the primitive pair space, we introduce PSO algorithm to complete the optimization for the first time. And in the end of this paper, we give the precision and efficiency evaluation about the algorithm and find it might be a reasonable choice for deformable models in collision detection
File Size	1481055
File Format	PDF
ISBN	1424400287
DOI	10.1109/MMMC.2006.1651342
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Deformable models Particle swarm optimization Sampling methods Acceleration Solid modeling Focusing Object detection Educational institutions Laboratories
Content Type	Text
Resource Type	Article

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Stochastic collision detection between deformable models using particle swarm optimization algorithm